Visual-spatial exploration of thematic spaces: A comparative study of three visualisation models

T. Cribbin, C. Chen

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


Scatter graphs (e.g. SPIRE Galaxies, Bead, VR-VIBE) are a popular medium for visualising spatial-semantic structures derived from abstract information spaces. For small spaces (i.e. less than one hundred nodes), such graphs can be an effective means of reducing high-dimensional information into two or three spatial dimensions. As dimensionality increases, representing the thematic diversity of documents using spatial proximity alone becomes less and less effective. This paper reports an experiment designed to determine whether, for larger spaces, benefits are to be gained from adding visual links between document nodes as an additional means of representing the most important semantic relationships. Two well known algorithms, minimum spanning trees (MST) and pathfinder associative networks (PFNET), were tested against both a scatter graph visualisation, derived from factor analysis, and a traditional list-based hypertext interface. It was hypothesised that visual links would facilitate users' comprehensi on of the information space with corresponding gains in information seeking performance. Navigation performance and user impressions were analysed across a range of different search tasks. Results indicate both significant performance gains and more positive user feedback for MST and PFNET visualisations over scatter graphs. Performance on all visualisations was generally poorer and never better than that achieved on the text list interface, although the magnitude of these differences was found to be highly task dependent.

Original languageEnglish
Pages (from-to)199-209
Number of pages11
JournalProceedings of SPIE - The International Society for Optical Engineering
Publication statusPublished - 2001
EventVisual Data Exploration and Analysis VIII - San Jose, CA, United States
Duration: 2001 Jan 222001 Jan 23

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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